Using Simulation to Improve Nurse Practitioner Education Regarding Opioid Prescribing and Medical Assistance in Dying: A Quality Improvement Project
Bibliographic record
Abstract
Aim: A Quality Improvement Project, guided by the Service-Learning framework, was undertaken to determine if introducing simulation into graduate nurse practitioner (NP) education would improve students’ knowledge, self-reported competency, and confidence regarding opioid prescribing and participation in Medical Assistance in Dying (MAiD). Background: Canadian government regulations authorize NPs to prescribe opioids and participate in MAiD. Simulation-based learning provides an opportunity for NP students to improve knowledge and critical-thinking skills regarding MAiD protocols and opioid prescribing in a safe, non-judgmental environment. Methods: A four-hour simulation-based workshop on opioid prescribing and MAiD was provided to NP students in their final course before graduation. NP students rotated through three 60-minute simulation-based scenario stations; two opioid scenarios using standardized patients and a MAiD scenario with a high-fidelity manikin. Students were expected to apply knowledge obtained during their NP program to conduct a thorough assessment, determine diagnostic tests/tools, formulate diagnoses, and develop a collaborative treatment plan. Outcomes measures included completing a pre/post-simulation knowledge-based quiz, self-assessment on each scenario, and debriefing. Findings: Scores on the pre-simulation quiz score ranged from 3–9 (M = 6.19); post-simulation quiz scores ranged from 6-12 (M = 9.88). Paired-Samples T-Test indicated a statistically significant increase between pre and post-mean scores. In all scenarios, there was an increase in the percentage of NP students who self-reported themselves as “competent” between their pre/post-simulation assessments. Conclusions: This educational innovation created an engaging environment that facilitated learning. Given that opioid prescribing and MAiD are authorized acts for NPs, it is essential that graduates feel supported and prepared for these situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".